Space-Time Analysis of Dynamic Scenes

The main objective of the presented research is to analyze video data of
dynamic scenes based on their behavioral content. We first extract
independent space-time components, and then analyze and classify
them. Each spatio-temporal component spatially corresponds
to a scene component having consistent motion (e.g., a static
background scene versus a foreground moving object), and
temporally corresponds to consistent behavior over time, i.e., a
single action or event. For example, if a person first walks and
then starts running, this corresponds to two different
temporal events, hence two different temporal components. The
proposed methods use only temporal information cues and ignore
appearance effects.

The extraction and classification of independent space-time
components is done using two different approaches. The first
approach, which we refer to as the "factorization approach" or
the "subspace-based approach", is geometric in its nature and
is based on linear subspace constraints. The second approach, to
which we refer as the "statistical approach", is probabilistic
and regards an event as a stochastic process. These approaches and
their applications will be presented in this talk.